Knowledge catalogue
arXiv:2607.12246v1 Announce Type: new Abstract: Personalization in two-sided marketplaces relies heavily on user-level features, yet for platforms with infrequent, high-consideration purchases, a larg
arXiv:2607.12292v1 Announce Type: new Abstract: Crack segmentation is essential for infrastructure inspection and structural health assessment, but existing high-performance methods typically require
arXiv:2607.11897v1 Announce Type: new Abstract: Linear attention replaces softmax attention's growing KV cache with a fixed recurrent state, but this compression limits exact state tracking and long-c
arXiv:2607.11890v1 Announce Type: cross Abstract: Open-ended surveys offer valuable insights, but they are notoriously difficult to analyze at scale. Building on previous work that employed traditiona
arXiv:2607.12826v1 Announce Type: new Abstract: This paper addresses Carl Hempel's longstanding problem of statistical ambiguity in inductive-statistical inference, in which contradictory predictions
arXiv:2607.12104v1 Announce Type: cross Abstract: Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production trac
arXiv:2607.12267v1 Announce Type: cross Abstract: Language agents that interleave reasoning and tool use degrade sharply as reasoning chains lengthen, even when each individual step is easy. We trace
arXiv:2607.12612v1 Announce Type: new Abstract: BERT models have revolutionised Natural Language Processing (NLP) through their ability to process unstructured text across diverse domains. However, de
🆕 5 Trends That Defined AI Engineering at World’s Fair 2026 https://latent.space/p/aiewf26trends @ricmac's big recap of @aidotengineer: 1. The focus shifts from agents to systems 2. Loop engineering i
At Observe 2026, Jason Lopatecki and Aparna Dhinakaran described the shift from human-operated agent development to systematic agent improvement—and what builders should change in their stacks first.
congrats on the launch! As the task horizons on agents gets longer, we need more work evaluating and training models to be better in long-running, open-ended, evolving real-world environments. Today w
Ever wanted to quickly turn a PDF into clean text to paste into your favorite AI agent, without having use CLIs or open the browser? We built exactly that. Using @TauriAp ps, with a Rust backend power
Microsoft published a 109-page technical report on MAI-Thinking-1. Here’s the abbreviated version of how a modern lab actually trains a frontier reasoning model — from scraping the web to reinforcemen
New model for AMD Strix Halo users: My 198B Step 3.7 Flash release was a big hit, but this one may be even better: 298B-parameter Hy3, now running on a new 2-bit FPX codebook designed to map efficient
This post likely reflects on the distinct emotional or relational significance of being called 'daddy' by a bisexual male partner, suggesting that the experience carries different meaning or impact co
arXiv:2607.08741v1 Announce Type: cross Abstract: Generating realistic 3D human motions in real-time within interactive applications is key for animation, simulation, and humanoid robotics. While rece
arXiv:2607.08252v1 Announce Type: new Abstract: Long-term persona agents must remain identifiable while adapting to new events, relationships, evidence, and social conditions. We identify self-locking
arXiv:2607.08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properti
arXiv:2607.08766v1 Announce Type: new Abstract: We propose OPSD-V, an on-policy self-distillation paradigm for post-training few-step autoregressive (AR) video diffusion models. Existing few-step AR v
arXiv:2502.15543v4 Announce Type: replace-cross Abstract: Large language models (LLMs) integrated with retrieval-augmented generation (RAG) have improved factuality by grounding outputs in external ev
arXiv:2607.07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast. Here we develop and ev
arXiv:2607.08691v1 Announce Type: cross Abstract: Repository-level code generation requires implementing target functions while accounting for complex cross-file dependencies and project-specific conv
arXiv:2607.08283v1 Announce Type: new Abstract: Vision--Language--Action (VLA) policies such as pi_{0.5} and OpenVLA perform well on many manipulation tasks, but they are often reactive: the next acti
arXiv:2607.08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish. Consequently, improving Large Language Model
arXiv:2607.08032v1 Announce Type: new Abstract: Large language models, and the agents built on them, spend an ever-growing share of their compute and memory on remembering: caching attention keys and
arXiv:2607.08740v1 Announce Type: new Abstract: Large language model (LLM) applications increasingly use explicit workflows for tool use, retrieval, branching, checkpointing, and human approval. Exist
arXiv:2607.07475v1 Announce Type: new Abstract: In robotics, the capability of an artificial agent to represent the range of its action possibilities, i.e. affordances, is crucial to understand how it
arXiv:2607.07251v1 Announce Type: new Abstract: One of the expected abilities of vision-language models (VLMs) is spatial reasoning ability based on a given text and image. To evaluate the spatial rea
arXiv:2607.06641v1 Announce Type: cross Abstract: Large language models (LLMs) achieve promising results on medical question answering benchmarks, yet their use in public health is constrained by hall
arXiv:2607.07507v1 Announce Type: cross Abstract: Hallucinations in vision language models (VLMs) are commonly treated as semantic errors, yet they often arise from partial or ambiguous visual evidenc
arXiv:2607.07135v1 Announce Type: new Abstract: Contrastive Language-Image Pre-training (CLIP) relies on softmax-based self-attention, a strictly positive distribution that assigns probability mass to
arXiv:2607.06906v1 Announce Type: new Abstract: Agentic AI development today runs on token maxing: buying capability with tokens -- longer reasoning traces, more turns, wider tool payloads, bigger rep
arXiv:2607.06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot dem
arXiv:2607.06054v1 Announce Type: cross Abstract: Off-the-shelf TTS systems are poorly adapted to Taiwanese Mandarin. Their accent defaults to other Mandarin variants, their tokenizers over-segment co
arXiv:2607.05571v1 Announce Type: new Abstract: Large language models are increasingly explored as AI tutors, yet deploying them in K-12 settings raises concerns around privacy, cost, and reliance on
arXiv:2607.06296v1 Announce Type: cross Abstract: This paper presents the design and evaluation of a maintainable hybrid generative architecture for automated music harmony generation from melody. The
arXiv:2607.06269v1 Announce Type: new Abstract: Current large language models (LLMs) are fundamentally stateless: their behavior is fully determined by input at inference time, and any higher-order co
arXiv:2607.05988v1 Announce Type: new Abstract: Multispectral image(MSI) and hyperspectral image(HSI) object tracking object tracking exploits recorded band-wise observations to improve target--backgr
arXiv:2603.23115v2 Announce Type: replace Abstract: The realism of AI-generated images (AIGI) poses increasing challenges for reliable forensic detection, where heterogeneous expert detectors may prod
arXiv:2607.03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas. While sub
arXiv:2607.05348v1 Announce Type: new Abstract: Open-vocabulary 3D scene understanding aims to segment 3D scenes beyond predefined categories by transferring semantic knowledge from vision-language mo
arXiv:2607.02911v1 Announce Type: cross Abstract: LLM-based coding agents solve software-engineering tasks through iterative interactions with development environments, where returned observations acc
arXiv:2607.04140v1 Announce Type: cross Abstract: Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness by
arXiv:2607.03821v1 Announce Type: cross Abstract: Personal AI agents that run on the user's local machine, such as OpenClaw, automate daily tasks including web search, email, and file management. Thei
arXiv:2607.03870v1 Announce Type: new Abstract: As LLMs generate increasingly long outputs, effective uncertainty estimation must identify errors at fine-grained levels rather than discard entire resp
arXiv:2607.03822v1 Announce Type: new Abstract: Vision-based 3D occupancy prediction fundamentally relies on the 2D-to-3D view transformation. Current paradigms predominantly utilize explicit physical
arXiv:2607.04856v1 Announce Type: new Abstract: Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurren
arXiv:2607.02770v1 Announce Type: cross Abstract: We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute effi
arXiv:2607.02959v1 Announce Type: new Abstract: We introduce VSeek, an agentic framework that transforms long-video question answering (LVQA) from a passive, single-pass perception task into a multi-t
arXiv:2511.20446v3 Announce Type: replace Abstract: The way humans interact with each other, including interpersonal distances, spatial configuration, and motion, varies significantly across different
arXiv:2607.04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over
arXiv:2607.04617v1 Announce Type: new Abstract: Long-lived AI agents require continuity across interactions, but continuity cannot be obtained by simply extending the prompt window. An agent must pres
arXiv:2607.04371v1 Announce Type: new Abstract: We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maxim
arXiv:2607.03925v1 Announce Type: new Abstract: EEG foundation models have shown strong potential in learning generalized representations across subjects and tasks. However, most existing approaches f
arXiv:2607.04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical respons
For years, enterprise AI has been synonymous with conversational AI — the customer service bots and digital assistants we interact with every day. But today, the market has shifted. We’ve officially m
arXiv:2607.04439v1 Announce Type: new Abstract: Large language models have made research ideation increasingly accessible, yet effective idea development requires more than generating candidate direct
arXiv:2607.02922v1 Announce Type: new Abstract: Video reasoning segmentation demands pixel-accurate object tracking across hundreds of frames under complex natural language queries, producing dense sp
arXiv:2603.20659v2 Announce Type: replace Abstract: Large scale pre-training on text and image data along with diverse robot demonstrations has helped Vision Language Action models (VLAs) to generaliz
arXiv:2607.05339v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) is effective when the current policy already samples useful reasoning trajectories, but it stalls on hard pr